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SyncMask: Synchronized Attentional Masking for Fashion-centric vision-Language Pretraining

SyncMask: Synchronized Attentional Masking for Fashion-centr...

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Song, Chull Hwan Hwang, Taebaek Yoon, Jooyoung Choi, Shunghyun Gu, Yeong Hyeon Dealicious Inc Seoul South Korea Sejong Univ Seoul South Korea

ISBN: (纸本)9798350353006

vision-language models (VLMs) have made significant strides in cross-modal understanding through large-scale paired datasets. However, in fashion domain, datasets of-en exhibit a disparity between the information conveyed in image and text. This issue stems from datasets containing multiple images of a single fashion item all paired with one text, leading to cases where some textual details are not visible in individual images. This mismatch, particularly when non-co-occurring elements are masked, undermines the training of conventional VLM objectives like Masked Language Modeling and Masked Image Modeling, thereby hindering the model's ability to accurately align fine-grained visual and textual features. Addressing this problem, we pro-pose Synchronized attentional Masking (SyncMask), which generate masks that pinpoint the image patches and word tokens where the information co-occur in both image and text. This synchronization is accomplished by harnessing cross-attentional features obtained from a momentum model, ensuring a precise alignment between the two modalities. Additionally, we enhance grouped batch sampling with semi-hard negatives, effectively mitigating false negative issues in Image-Text Matching and Image-Text Contrastive learning objectives within fashion datasets. Our experiments demonstrate the effectiveness of the proposed approach, outperforming existing methods in three downstream tasks.

关键词： Visual languages

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USE: Universal Segment Embeddings for Open-Vocabulary Image Segmentation

USE: Universal Segment Embeddings for Open-Vocabulary Image ...

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Wang, Xiaoqi He, Wenbin Xuan, Xiwei Sebastian, Clint Ono, Jorge Piazentin Li, Xin Behpour, Sima Thang Doan Gou, Liang Shen, Han-Wei Ren, Liu Bosch Res North Amer Oak Brook Terrace IL 60181 USA Bosch Ctr Artificial Intelligence Baden Baden Germany Ohio State Univ Columbus OH 43210 USA Univ Calif Davis Davis CA 95616 USA

ISBN: (纸本)9798350353013;9798350353006

The open-vocabulary image segmentation task involves partitioning images into semantically meaningful segments and classifying them with flexible text-defined categories. The recent vision-based foundation models such as the Segment Anything Model (SAM) have shown superior performance in generating class-agnostic image segments. The main challenge in open-vocabulary image segmentation now lies in accurately classifying these segments into text-defined categories. In this paper, we introduce the Universal Segment Embedding (USE) framework to address this challenge. This framework is comprised of two key components: 1) a data pipeline designed to efficiently curate a large amount of segment-text pairs at various granularities, and 2) a universal segment embedding model that enables precise segment classification into a vast range of text-defined categories. The USE model can not only help open-vocabulary image segmentation but also facilitate other downstream tasks (e.g., querying and ranking). Through comprehensive experimental studies on semantic segmentation and part segmentation benchmarks, we demonstrate that the USE framework outperforms state-of-the-art open-vocabulary segmentation methods.

关键词： foundation model open-vocabulary image segmentation vision-language model

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Beyond Image Super-Resolution for Image recognition with Task-Driven Perceptual Loss

Beyond Image Super-Resolution for Image Recognition with Tas...

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Kim, Jaeha Oh, Junghun Lee, Kyoung Mu Seoul Natl Univ Dept ECE Seoul South Korea Seoul Natl Univ ASRI Seoul South Korea Seoul Natl Univ IPAI Seoul South Korea

ISBN: (纸本)9798350353013;9798350353006

In real-world scenarios, image recognition tasks, such as semantic segmentation and object detection, often pose greater challenges due to the lack of information available within low-resolution (LR) content. Image super-resolution (SR) is one of the promising solutions for addressing the challenges. However, due to the ill-posed property of SR, it is challenging for typical SR methods to restore task-relevant high-frequency contents, which may dilute the advantage of utilizing the SR method. Therefore, in this paper, we propose Super-Resolution for Image recognition (SR4IR) that effectively guides the generation of SR images beneficial to achieving satisfactory image recognition performance when processing LR images. The critical component of our SR4IR is the task-driven perceptual (TDP) loss that enables the SR network to acquire task-specific knowledge from a network tailored for a specific task. Moreover, we propose a cross-quality patch mix and an alternate training framework that significantly enhances the efficacy of the TDP loss by addressing potential problems when employing the TDP loss. Through extensive experiments, we demonstrate that our SR4IR achieves outstanding task performance by generating SR images useful for a specific image recognition task, including semantic segmentation, object detection, and image classification. The implementation code is available at https://***/JaehaKim97/SR4IR.

关键词： Low-level vision Perceptual loss Super-resolution Task-aware restoration

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Efficient Test-Time Adaptation of vision-Language Models

Efficient Test-Time Adaptation of Vision-Language Models

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Karmanov, Adilbek Guan, Dayan Lu, Shijian El Saddik, Abdulmotaleb Xing, Eric Mohamed bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates Nanyang Technol Univ Singapore Singapore Univ Ottawa Ottawa ON Canada Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9798350353006

Test-time adaptation with pre-trained vision-language models has attracted increasing attention for tackling distribution shifts during the test time. Though prior studies have achieved very promising performance, they involve intensive computation which is severely unaligned with test-time adaptation. We design TDA, a training-free dynamic adapter that enables effective and efficient test-time adaptation with vision-language models. TDA works with a lightweight key-value cache that maintains a dynamic queue with few-shot pseudo labels as values and the corresponding test-sample features as keys. Leveraging the key-value cache, TDA allows adapting to test data gradually via progressive pseudo label refinement which is super-efficient without incurring any backpropagation. In addition, we introduce negative pseudo labeling that alleviates the adverse impact of pseudo label noises by assigning pseudo labels to certain negative classes when the model is uncertain about its pseudo label predictions. Extensive experiments over two benchmarks demonstrate TDA's superior effectiveness and efficiency as compared with the state-of- the-art. The code has been released in https://***/tda/.

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StableVITON: Learning Semantic Correspondence with Latent Diffusion Model for Virtual Try-On

StableVITON: Learning Semantic Correspondence with Latent Di...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Kim, Jeongho Gu, Gyojung Park, Minho Park, Sunghyun Choo, Jaegul Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9798350353013;9798350353006

Given a clothing image and a person image, an image-based virtual try-on aims to generate a customized image that appears natural and accurately reflects the characteristics of the clothing image. In this work, we aim to expand the applicability of the pre-trained diffusion model so that it can be utilized independently for the virtual try-on task. The main challenge is to preserve the clothing details while effectively utilizing the robust generative capability of the pre-trained model. In order to tackle these issues, we propose StableVITON, learning the semantic correspondence between the clothing and the human body within the latent space of the pre-trained diffusion model in an end-to-end manner. Our proposed zero cross-attention blocks not only preserve the clothing details by learning the semantic correspondence but also generate high-fidelity images by utilizing the inherent knowledge of the pre-trained model in the warping process. Through our proposed novel attention total variation loss and applying augmentation, we achieve the sharp attention map, resulting in a more precise representation of clothing details. StableVITON out-performs the baselines in qualitative and quantitative eval-uation, showing promising quality in arbitrary person images. Our code is available at https://***/ rlawjdghek/StableVITON.

关键词： computer vision Diffusion Model Virtual Try-On

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Flexible Biometrics recognition: Bridging the Multimodality Gap through Attention, Alignment and Prompt Tuning

Flexible Biometrics Recognition: Bridging the Multimodality ...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Tiong, Leslie Ching Ow Sigmund, Dick Chan, Chen-Hui Teoh, Andrew Beng Jin Samsung Elect Suwon South Korea AIDOT Inc Seoul South Korea Korea Inst Sci & Technol Seoul South Korea Yonsei Univ Seoul South Korea

ISBN: (纸本)9798350353013;9798350353006

Periocular and face are complementary biometrics for identity management, albeit with inherent limitations, notably in scenarios involving occlusion due to sunglasses or masks. In response to these challenges, we introduce Flexible Biometric recognition (FBR), a novel framework designed to advance conventional face, periocular, and multimodal face-periocular biometrics across both intra- and cross-modality recognition tasks. FBR strategically utilizes the Multimodal Fusion Attention (MFA) and Multimodal Prompt Tuning (MPT) mechanisms within the vision Transformer architecture. MFA facilitates the fusion of modalities, ensuring cohesive alignment between facial and periocular embeddings while incorporating soft-biometrics to enhance the model's ability to discriminate between individuals. The fusion of three modalities is pivotal in exploring interrelationships between different modalities. Additionally, MPT serves as a unifying bridge, intertwining inputs and promoting cross-modality interactions while preserving their distinctive characteristics. The collaborative synergy of MFA and MPT enhances the shared features of the face and periocular, with a specific emphasis on the ocular region, yielding exceptional performance in both intra- and cross-modality recognition tasks. Rigorous experimentation across four benchmark datasets validates the noteworthy performance of the FBR model. The source code is available at https://***/MIS-DevWorks/FBR.

关键词： cross-modality flexible biometrics recognition multimodal prompt tuning

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Sequential Modeling Enables Scalable Learning for Large vision Models

Sequential Modeling Enables Scalable Learning for Large Visi...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Bail, Yutong Geng, Xinyang Mangalam, Karttikeya Bar, Amir Yuille, Alan L. Darrell, Trevor Malik, Jitendra Efros, Alexei A. UC Berkeley BAIR Berkeley CA 94720 USA Johns Hopkins Univ Baltimore MD 21218 USA

ISBN: (纸本)9798350353006

We introduce a novel sequential modeling approach which enables learning a Large vision Model (LVM) without making use of any linguistic data. To do this, we define a common format, "visual sentences", in which we can represent raw images and videos as well as annotated data sources such as semantic segmentations and depth reconstructions with-out needing any meta-knowledge beyond the pixels. Once this wide variety of visual data (comprising 420 billion to-kens) is represented as sequences, the model can be trained to minimize a cross-entropy loss for next token prediction. By training across various scales of model architecture and data diversity, we provide empirical evidence that our models scale effectively. Many different vision tasks can be solved by designing suitable visual prompts at test time.

关键词： pretraining scaling Self-supervised Learning

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PELA: Learning Parameter-Efficient Models with Low-Rank Approximation

PELA: Learning Parameter-Efficient Models with Low-Rank Appr...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Guo, Yangyang Wang, Guangzhi Kankanhalli, Mohan Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9798350353006

Applying a pre-trained large model to downstream tasks is prohibitive under resource-constrained conditions. Re-cent dominant approaches for addressing efficiency issues involve adding a few learnable parameters to the fixed backbone model. This strategy, however, leads to more challenges in loading large models for downstream fine-tuning with limited resources. In this paper, we propose a novel method for increasing the parameter efficiency of pre-trained models by introducing an intermediate pre-training stage. To this end, we first employ low-rank approximation to compress the original large model and then devise a feature distillation module and a weight perturbation regularization module. These modules are specifically designed to enhance the low-rank model. In particular, we update only the low-rank model while freezing the backbone parameters during pre-training. This allows for direct and efficient utilization of the low-rank model for downstream fine-tuning tasks. The proposed method achieves both efficiencies in terms of required parameters and computation time while maintaining comparable results with minimal modifications to the backbone architecture. Specifically, when applied to three vision-only and one vision-language Transformer models, our approach often demonstrates a merely similar to 0.6 point decrease in performance while reducing the original parameter size by 1/3 to 2/3. We release our code at link.

关键词： Knowledge Distillation Low-rank Approximation vision-Language

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Evaluating the Integration of Morph Attack Detection in Automated Face recognition Systems

Evaluating the Integration of Morph Attack Detection in Auto...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Panzino, Andrea la Cava, Simone Maurizio Orru, Giulia Marcialis, Gian Luca Univ Cagliari Piazza Armi I-09123 Cagliari Italy

ISBN: (纸本)9798350365474

Due to the possibility of automatically verifying an individual's identity by comparing his/her face with that present in a personal identification document, systems providing identification must be equipped with digital manipulation detectors. Morphed facial images can be considered a threat among other manipulations because they are visually indistinguishable from authentic facial photos. They can have characteristics of many possible subjects due to the nature of the attack. Thus, morphing attack detection methods (MADs) must be integrated into automated face recognition. Following the recent advances in MADs, we investigate their effectiveness by proposing an integrated system simulator of real application contexts, moving from known to never-seen-before attacks.

关键词： detection face integration morphing

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Finding Lottery Tickets in vision Models via Data-driven Spectral Foresight Pruning

Finding Lottery Tickets in Vision Models via Data-driven Spe...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Iurada, Leonardo Ciccone, Marco Tommasi, Tatiana Politecn Torino Turin Italy

ISBN: (纸本)9798350353006

Recent advances in neural network pruning have shown how it is possible to reduce the computational costs and memory demands of deep learning models before training. We focus on this framework and propose a new pruning at initialization algorithm that leverages the Neural Tangent Kernel (NTK) theory to align the training dynamics of the sparse network with that of the dense one. Specifically, we show how the usually neglected data-dependent component in the NTK's spectrum can be taken into account by providing an analytical upper bound to the NTK's trace obtained by decomposing neural networks into individual paths. This leads to our Path eXclusion (PX), a foresight pruning method designed to preserve the parameters that mostly influence the NTK's trace. PX is able to find lottery tickets (i.e. good paths) even at high sparsity levels and largely reduces the need for additional training. When applied to pre-trained models it extracts subnetworks directly usable for several downstream tasks, resulting in performance comparable to those of the dense counterpart but with substantial cost and computational savings. Code available at: https://***/iurada/px-ntk-pruning

关键词： Efficient computer vision Neural Network Pruning Neural Tangent Kernel Pruning-at-Initialization

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